Climate Voting in Washington State

1 Introduction

2 Data & Methods

2.1 Data Sources

• Voting Precinct Shapefiles: https://www.sos.wa.gov/elections/data-research/election-data-and-maps/reports-data-and-statistics/precinct-shapefiles
• Election Results: https://www.sos.wa.gov/elections/data-research/election-data-and-maps/election-results-and-voters-pamphlets
• American Community Survey: https://www.census.gov/programs-surveys/acs/data.html
• 2017 Local Area Transportation Characteristics for Households https://www.bts.gov/latch/latch-data

2.2 Data

Rows: 1,454
Columns: 5
Rowwise: 
$ geoid           <chr> "53001950200", "53005010600", "53005010902", "53007960…
$ hh_vmt          <dbl> 49.22, 39.50, 36.03, 52.59, 39.31, 31.31, 40.17, 57.66…
$ vote_rep_pct    <dbl> 77.975856, 52.493079, 57.784743, 62.749887, 53.021292,…
$ vote_i0732n_pct <dbl> 78.69265, 65.03399, 71.65084, 72.74983, 65.13168, 60.5…
$ geometry        <GEOMETRY [m]> POLYGON ((-3507230 8109216,..., POLYGON ((-35…

Source: Article Notebook

Data summary

Name	model_data_skim
Number of rows	1431
Number of columns	4
_______________________
Column type frequency:
character	1
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
geoid	0	1	11	11	0	1431	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
hh_vmt	0	1	41.36	8.28	13.48	35.78	41.36	47.48	61.76	▁▂▇▇▂
vote_rep_pct	0	1	39.22	16.51	2.15	27.66	40.49	50.94	81.60	▃▆▇▅▁
vote_i0732n_pct	0	1	59.71	11.28	23.45	53.13	61.15	67.67	87.60	▁▂▇▇▂

Source: Article Notebook

Figure 1

Source: Article Notebook

Figure 2

Source: Article Notebook

Figure 3

Source: Article Notebook

Figure 4

Source: Article Notebook

2.3 Models

2.3.1 Ordinary Least Squares (OLS)

OLS linear regression model:

Parameter    | Coefficient |       SE |         95% CI | t(1428) |      p
-------------------------------------------------------------------------
(Intercept)  |       28.86 |     0.42 | [28.05, 29.68] |   69.36 | < .001
hh vmt       |        0.16 |     0.01 | [ 0.14,  0.18] |   14.52 | < .001
vote rep pct |        0.61 | 5.62e-03 | [ 0.60,  0.63] |  109.46 | < .001

Source: Article Notebook

Linear model assumption checks:

Figure 5

Source: Article Notebook

Spatial Autocorrelation check (Moran I test):

    Moran I test under randomisation

data:  residuals(model_lm)  
weights: model_spatial_weights  
n reduced by no-neighbour observations  

Moran I statistic standard deviate = 29.982, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance 
     0.4884553310     -0.0007007708      0.0002661758 

Source: Article Notebook

2.3.2 Spatially Lagged Regression

Call:
lagsarlm(formula = model_lm, data = model_data, listw = model_spatial_weights, 
    zero.policy = TRUE)

Residuals:
     Min       1Q   Median       3Q      Max 
-19.7908  -1.6062   0.1467   1.8119  18.9670 

Type: lag 
Regions with no neighbours included:
 523 1102 1253 
Coefficients: (asymptotic standard errors) 
              Estimate Std. Error z value  Pr(>|z|)
(Intercept)  19.340830   0.681397  28.384 < 2.2e-16
hh_vmt        0.151437   0.010106  14.984 < 2.2e-16
vote_rep_pct  0.454746   0.010966  41.468 < 2.2e-16

Rho: 0.27185, LR test value: 276.53, p-value: < 2.22e-16
Asymptotic standard error: 0.016331
    z-value: 16.646, p-value: < 2.22e-16
Wald statistic: 277.09, p-value: < 2.22e-16

Log likelihood: -3504.669 for lag model
ML residual variance (sigma squared): 7.7375, (sigma: 2.7816)
Nagelkerke pseudo-R-squared: 0.93831 
Number of observations: 1431 
Number of parameters estimated: 5 
AIC: 7019.3, (AIC for lm: 7293.9)
LM test for residual autocorrelation
test value: 405.56, p-value: < 2.22e-16

Source: Article Notebook

Parameter comparison: OLS vs Spatial Lag

Parameter    |             model_lm |    model_spatial_lag
----------------------------------------------------------
(Intercept)  | 28.86 (28.05, 29.68) | 19.34 (18.01, 20.68)
hh vmt       |  0.16 ( 0.14,  0.18) |  0.15 ( 0.13,  0.17)
vote rep pct |  0.61 ( 0.60,  0.63) |  0.45 ( 0.43,  0.48)
rho          |                      |  0.27 ( 0.24,  0.30)
----------------------------------------------------------
Observations |                 1431 |                     

Source: Article Notebook

Comparison of Adjusted R²/Pseudo Adjusted R²: OLS vs Spatial Lag

# A tibble: 1 × 2
     lm spatial_lag
  <dbl>       <dbl>
1 0.925       0.938

Source: Article Notebook

Spatially lagged regression model residuals:

Source: Article Notebook

2.4 Methodology Notes

• Income should not be included in our regression because it is used in the model that estimates household VMT (see LATCH Methodology p. 10)

Figure 1: Figure 5: